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Error Bars Represent Standard Deviation
Correspondence ExpressionBlast: mining large, unstructured expression databases Points of Significance: Error bars Martin Krzywinski1, Naomi Altman2, Affiliations Journal name: Nature Methods Volume: 10, Pages: 921–922 Year published: (2013) DOI: doi:10.1038/nmeth.2659 Published online 27 September 2013 Article tools what do error bars represent PDF PDF Download as PDF (269 KB) View interactive PDF in ReadCube Citation Reprints Rights & permissions Article metrics The meaning of error bars is often misinterpreted, as is the statistical significance of their overlap. Subject terms: Publishing• Research data• Statistical methods At a glance Figures View all figures Figure 1: Error bar width and interpretation of spacing depends on the error bar type. (a,b) Example graphs are based on sample means of 0 and what do error bars represent on a bar graph 1 (n = 10). (a) When bars are scaled to the same size and abut, P values span a wide range. When s.e.m. bars touch, P is large (P = 0.17). (b) Bar size and relative position vary greatly at the conventional P value significance cutoff of 0.05, at which bars may overlap or have a gap. Full size image View in article Figure 2: The size and position of confidence intervals depend on the sample. On average, CI% of intervals are expected to span the mean—about 19 in 20 times for 95% CI. (a) Means and 95% CIs of 20 samples (n = 10) drawn from a normal population with mean m and s.d. σ. By chance, two of the intervals (red) do not capture the mean. (b) Relationship between s.e.m. and 95% CI error bars with increasing n. Full size image View in article Figure 3: Size and position of s.e.m. and 95% CI error bars for common P values. Examples are based on sample means of 0 and 1 (n = 10). Full size image View in article Last month in Points of Significance, we showed how samples are used to estimate population statistics. We emphasized that, because of chance, our estimates had an uncertainty. This month we focus on how uncertainty is represented in scientific publications and reveal several wa
or Standard error of mean) - survival curve of C. elegans (Oct/29/2009 )Visit this topic in live forum Printer Friendly VersionHi all. i would love to hear from different point of views regarding the title
What Does Error Bars Represent
above. currently i am working onto the survival curve of c. elegans. however, how to interpret error bars i was quite confused whether i should use Stand. deviation or stand. error of mean when plotting the error bar in
Large Error Bars
my graph. some researchers have used S.D, some used S.E.M. anyone have idea onto this ? Thank you. -tyrael- tyrael on Oct 30 2009, 08:48 AM said:Hi all. i would love to hear http://www.nature.com/nmeth/journal/v10/n10/full/nmeth.2659.html from different point of views regarding the title above. currently i am working onto the survival curve of c. elegans. however, i was quite confused whether i should use Stand. deviation or stand. error of mean when plotting the error bar in my graph. some researchers have used S.D, some used S.E.M. anyone have idea onto this ? Thank you. 0 In my opinion Error is best represented http://www.protocol-online.org/biology-forums-2/posts/11239.html by the Standard error!!!
-Pradeep Iyer- FROM BMJ The terms "standard error" and "standard deviation" are often confused.1 The contrast between these two terms reflects the important distinction between data description and inference, one that all researchers should appreciate. The standard deviation (often SD) is a measure of variability. When we calculate the standard deviation of a sample, we are using it as an estimate of the variability of the population from which the sample was drawn. For data with a normal distribution,2 about 95% of individuals will have values within 2 standard deviations of the mean, the other 5% being equally scattered above and below these limits. Contrary to popular misconception, the standard deviation is a valid measure of variability regardless of the distribution. About 95% of observations of any distribution usually fall within the 2 standard deviation limits, though those outside may all be at one end. We may choose a different summary statistic, however, when data have a skewed distribution.3 When we calculate the sample mean we are usually interested not in the mean of this particular sample, but in the mean for individuals of this type--in statistical terms, of the population from which the sample comes. We usualopposed to a standard deviation? When plugging in errors for a simple bar chart of mean values, what are the statistical rules for https://www.researchgate.net/post/When_should_you_use_a_standard_error_as_opposed_to_a_standard_deviation which error to report? I guess the correct statistical test will render http://betterposters.blogspot.com/2012/01/error-bars.html this irrelevant, but it would still be good to know what to present in graphs. Topics Graphs × 706 Questions 3,038 Followers Follow Standard Deviation × 238 Questions 19 Followers Follow Standard Error × 119 Questions 11 Followers Follow Statistics × 2,247 Questions 90,290 Followers Follow Nov 5, 2013 error bars Share Facebook Twitter LinkedIn Google+ 4 / 1 Popular Answers Jochen Wilhelm · Justus-Liebig-Universität Gießen Very good advices above, but it leaves the essence of the question untouched. The CI is absolutly preferrable to the SE, but, however, both have the same basic meaing: the SE is just a 63%-CI. The SD, in contrast, has a different meaning. I suppose error bars represent the question is about which "meaning" should be presented. The SD is a property of the variable. It gives an impression of the range in which the values scatter (dispersion of the data). When this is important then show the SD. THE SE/CI is a property of the estimation (for instance the mean). The (frequentistic) interpretation is that the given proportion of such intervals will include the "true" parameter value (for instance the mean). Only 5% of 95%-CIs will not include the "true" values. If you want to show the precision of the estimation then show the CI. However, there is still a point to consider: Often, the estimates, for instance the group means, are actually not of particulat interest. Rather the differences between these means are the main subject of the investigation. Such differences (effects) are also estimates and they have their own SEs and CIs. Thus, showing the SEs or CIs of the groups indicates a measure of precision that is not relevant to the research question. The important thing to be shown here would be the differe
average, there should be an indication of how much smear there is in the data. It makes a huge difference to your interpretation of the information, particularly when glancing at the figure. For instance, I'm willing to bet most people looking at this... Would say, "Wow, the treatment is making a big difference compared to the control!" I'm likewise willing to bet most people looking at this (which plots the same averages)... Would say, "There's so much overlap in the data, there might not be any real difference between the control and the treatments." The problem is that error bars can represent at least three different measurements (Cumming et al. 2007). Standard deviation Standard error Confidence interval Sadly, there is no convention for which of the three one should add to a graph. There is no graphical convention to distinguish these three values, either. Here's a nice example of how different these three measures look (Figure 4 from Cumming et al. 2007), and how they change with sample size: I often see graphs with no indication of which of those three things the error bars are showing! And the moral of the story is: Identify your error bars! Put in the Y axis or in the caption for the graph. Reference Cumming G, Fidler F, Vaux D 2007. Error bars in experimental biology The Journal of Cell Biology 177(1): 7-11. DOI: 10.1083/jcb.200611141 A different problem with error bars is here. Posted by Zen Faulkes at 7:00 AM Labels: graphics 8 comments: Rafael Maia said... Thanks for posting on this very important, but often ignored, topic! A fundamental point is also that these measures of dispersion also represent very different information about the data and the estimation. While the standard deviation is a measure of variability of the data itself (how dispersed it is around its expected value), standard errors and CI refer to the variability or precision of the distribution of the statistic or estimate. That's why, in the figure you show, the SE and CI change with sample size but the SD doesn't: the SD is giving you information about the spread of the data, and the SE & CI are giving you information about how precise is your estimate of the mean. Thus, not only they affect the interpretation of the figure because they might give false impressions, but also because they actually mean different things! This makes your take-home message even more important: Identfy your error bars, or else we can't know what you mean!A rule of thumb I go by is: if you want to show how variable data are, you should show SDs; if you want to show how confident you are about something you're estimating, or the difference between es